

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase						)		->       int:
														A      :					int      		=					data






							def __iter__(					self						)		->       Dict:
														for element in self.data:
																					yield element






def        snake_case__		(    lowerCamelCase_=True		):
							A      :					Optional[Any]      		=					Accelerator(even_batches=lowerCamelCase_		)
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_ = False		):
							if iterable:
														A      :					List[str]      		=					DummyIterableDataset(torch.as_tensor(range(lowerCamelCase_		)		)		)
							else:
														A      :					Optional[int]      		=					TensorDataset(torch.as_tensor(range(lowerCamelCase_		)		)		)

							A      :					Optional[Any]      		=					DataLoader(lowerCamelCase_     ,				batch_size=lowerCamelCase_		)
							A      :					Dict      		=					accelerator.prepare(lowerCamelCase_		)

							return dl






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				):
							A      :					List[Any]      		=					create_dataloader(accelerator=lowerCamelCase_     ,				dataset_size=lowerCamelCase_     ,				batch_size=lowerCamelCase_		)

							A      :					List[str]      		=					[len(batch[0]		) for batch in dl]

							if accelerator.process_index == 0:
														assert batch_sizes == process_0_expected_batch_sizes
							elif accelerator.process_index == 1:
														assert batch_sizes == process_1_expected_batch_sizes






def        snake_case__		(    ):
							A      :					List[Any]      		=					create_accelerator()

							# without padding, we would expect a different number of batches
							verify_dataloader_batch_sizes(
							    lowerCamelCase_     ,				dataset_size=3     ,				batch_size=1     ,				process_0_expected_batch_sizes=[1, 1]     ,				process_1_expected_batch_sizes=[1, 1]     ,				)

							# without padding, we would expect the same number of batches, but different sizes
							verify_dataloader_batch_sizes(
							    lowerCamelCase_     ,				dataset_size=7     ,				batch_size=2     ,				process_0_expected_batch_sizes=[2, 2]     ,				process_1_expected_batch_sizes=[2, 2]     ,				)






def        snake_case__		(    ):
							A      :					str      		=					create_accelerator(even_batches=lowerCamelCase_		)

							verify_dataloader_batch_sizes(
							    lowerCamelCase_     ,				dataset_size=3     ,				batch_size=1     ,				process_0_expected_batch_sizes=[1, 1]     ,				process_1_expected_batch_sizes=[1]     ,				)

							verify_dataloader_batch_sizes(
							    lowerCamelCase_     ,				dataset_size=7     ,				batch_size=2     ,				process_0_expected_batch_sizes=[2, 2]     ,				process_1_expected_batch_sizes=[2, 1]     ,				)






def        snake_case__		(    ):
							A      :					Union[str, Any]      		=					create_accelerator(even_batches=lowerCamelCase_		)

							A      :					Optional[int]      		=					torch.nn.Linear(1     ,				1		)
							A      :					Dict      		=					accelerator.prepare(lowerCamelCase_		)

							A      :					Tuple      		=					create_dataloader(lowerCamelCase_     ,				dataset_size=3     ,				batch_size=1		)

							A      :					Optional[int]      		=					[]
							with accelerator.join_uneven_inputs([ddp_model]		):
														for batch_idx, batch in enumerate(lowerCamelCase_		):
																					A      :					Any      		=					ddp_model(batch[0].float()		)
																					A      :					Optional[Any]      		=					output.sum()
																					loss.backward()
																					batch_idxs.append(lowerCamelCase_		)

							accelerator.wait_for_everyone()

							if accelerator.process_index == 0:
														assert batch_idxs == [0, 1]
							elif accelerator.process_index == 1:
														assert batch_idxs == [0]






def        snake_case__		(    lowerCamelCase_		):
							with warnings.catch_warnings(record=lowerCamelCase_		) as w:
														with accelerator.join_uneven_inputs([Mock()]		):
																					pass

														assert issubclass(w[-1].category     ,				lowerCamelCase_		)
														assert "only supported for multi-GPU" in str(w[-1].message		)






def        snake_case__		(    ):
							A      :					Any      		=					True
							A      :					Union[str, Any]      		=					False
							A      :					str      		=					create_accelerator(even_batches=lowerCamelCase_		)
							A      :					Optional[int]      		=					torch.nn.Linear(1     ,				1		)
							A      :					Optional[Any]      		=					accelerator.prepare(lowerCamelCase_		)
							A      :					List[str]      		=					create_dataloader(lowerCamelCase_     ,				dataset_size=3     ,				batch_size=1		)
							A      :					int      		=					create_dataloader(lowerCamelCase_     ,				dataset_size=3     ,				batch_size=1		)

							with accelerator.join_uneven_inputs([ddp_model]     ,				even_batches=lowerCamelCase_		):
														A      :					List[Any]      		=					train_dl.batch_sampler.even_batches
														A      :					Tuple      		=					valid_dl.batch_sampler.even_batches

							assert train_dl_overridden_value == overridden_even_batches
							assert valid_dl_overridden_value == overridden_even_batches
							assert train_dl.batch_sampler.even_batches == default_even_batches
							assert valid_dl.batch_sampler.even_batches == default_even_batches






def        snake_case__		(    ):
							A      :					Tuple      		=					True
							A      :					Tuple      		=					False
							A      :					List[Any]      		=					create_accelerator(even_batches=lowerCamelCase_		)
							A      :					Tuple      		=					torch.nn.Linear(1     ,				1		)
							A      :					Optional[Any]      		=					accelerator.prepare(lowerCamelCase_		)
							create_dataloader(lowerCamelCase_     ,				dataset_size=3     ,				batch_size=1     ,				iterable=lowerCamelCase_		)
							A      :					List[str]      		=					create_dataloader(lowerCamelCase_     ,				dataset_size=3     ,				batch_size=1		)

							with warnings.catch_warnings():
														warnings.filterwarnings('''ignore'''		)
														try:
																					with accelerator.join_uneven_inputs([ddp_model]     ,				even_batches=lowerCamelCase_		):
																												A      :					Tuple      		=					batch_dl.batch_sampler.even_batches
														except AttributeError:
																					# ensure attribute error is not raised when processing iterable dl
																					raise AssertionError

							assert batch_dl_overridden_value == overridden_even_batches
							assert batch_dl.batch_sampler.even_batches == default_even_batches






def        snake_case__		(    ):
							A      :					Union[str, Any]      		=					create_accelerator()
							A      :					List[Any]      		=					torch.nn.Linear(1     ,				1		)
							A      :					Union[str, Any]      		=					accelerator.prepare(lowerCamelCase_		)
							create_dataloader(lowerCamelCase_     ,				dataset_size=3     ,				batch_size=1     ,				iterable=lowerCamelCase_		)

							with warnings.catch_warnings(record=lowerCamelCase_		) as w:
														with accelerator.join_uneven_inputs([ddp_model]     ,				even_batches=lowerCamelCase_		):
																					pass

														assert issubclass(w[-1].category     ,				lowerCamelCase_		)
														assert "only supported for map-style datasets" in str(w[-1].message		)






def        snake_case__		(    ):
							A      :					Union[str, Any]      		=					create_accelerator()

							accelerator.print('''Test that even_batches variable ensures uniform batches across processes'''		)
							test_default_ensures_even_batch_sizes()

							accelerator.print('''Run tests with even_batches disabled'''		)
							test_can_disable_even_batches()

							accelerator.print('''Test joining uneven inputs'''		)
							test_can_join_uneven_inputs()

							accelerator.print('''Test overriding even_batches when joining uneven inputs'''		)
							test_join_can_override_even_batches()

							accelerator.print('''Test overriding even_batches for mixed dataloader types'''		)
							test_join_can_override_for_mixed_type_dataloaders()

							accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders'''		)
							test_join_raises_warning_for_iterable_when_overriding_even_batches()

							accelerator.print('''Test join with non DDP distributed raises warning'''		)
							A      :					List[str]      		=					accelerator.state.distributed_type
							A      :					str      		=					DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase_		)
							A      :					List[Any]      		=					original_state


if __name__ == "__main__":
		main()









lowercase			:				Dict   =     [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					0
							while number:
														# Increased Speed Slightly by checking every 5 digits together.
														sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
														number //= 100000

							return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowercase			:				list[bool | None]   =     [None] * 10_00_00_00
lowercase			:				int   =     True
lowercase			:				Tuple   =     False






def        snake_case__		(    lowerCamelCase_		):

							if CHAINS[number - 1] is not None:
														return CHAINS[number - 1]  # type: ignore

							A      :					int      		=					chain(next_number(lowerCamelCase_		)		)
							A      :					Dict      		=					number_chain

							while number < 10000000:
														A      :					Any      		=					number_chain
														number *= 10

							return number_chain






def        snake_case__		(    lowerCamelCase_ = 10000000		):
							for i in range(1     ,				lowerCamelCase_		):
														if CHAINS[i] is None:
																					chain(i + 1		)

							return CHAINS[:number].count(lowerCamelCase_		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"{solution() = }")






